Abstract

The utilization of image-guidedradiotherapy(IGRT) technologies helps correct temporal and spatial deviations of the target volume relative to planned radiation beams. With the aid of these IGRT technologies, it becomes possible to better identify the target volume before and even during radiation treatment. However, since components of the detected deviations may be translational, rotational, and deformable, the question remains whether simple treatment-couch translational movement can be optimized to compensate for these complicated deviations. Deviation of the target volume and changes in patient body shape from that acquired for treatment planning may further add to the variations from planned dose distribution. In this study, an optimization strategy is developed to investigate these issues. The optimization process involved the use of the hill climbing algorithm, the detected target volume and patient body shape, and the dose distribution based on acquired images at treatment. During the process, the planned dose distribution was iteratively adjusted to reflect the changes of depth and distance as the translational treatment couch movement was being optimized. The optimal treatment couch movement was considered achieved when the highest fraction of the detected target volume was covered by prescription dose. This optimization strategy was evaluated on clinical prostate cancer cases. For each of the cases, cone beam computed tomography(CBCT)images were acquired right after fiducial marker-based kilovolt orthogonal imaging verification and setup adjustment. Based on the CBCTimages, the clinical target volume at the treatment was delineated and the translational treatment-couch movements were optimized with the developed strategy. The resultant dose coverage was compared to that without the optimization. The results showed that with the present strategy, rotational and deformable target deviations can be further compensated with translational couch correction.